Lab CudaVision - Lab Project¶
Learning Vision Systems on Graphics Cards (MA-INF 4308)
- Areesha Asif
- Abhishek S Pillai
Table of Contents¶
1 Import Modules¶
In [1]:
from torch.utils.data import Dataset, DataLoader
import os
import pickle
from PIL import Image
import torch
from torchvision import transforms
import matplotlib.pyplot as plt
import numpy as np
from EncoderCNN import Encoder
from ConvRNN import ConvLSTM
from Decoder import Decoder
from torchvision.utils import make_grid
import torch.optim as optim
from tqdm import tqdm
from MotionEncoder import MotionEncoder
from CameraEncoder import CameraEncoder
from RNN import EgoMotionRNN
from CameraDecoder import CameraDecoder
from DepthMap import DepthDecoder
from torch.utils.tensorboard import SummaryWriter
import numpy as np
import random
from PIL import ImageDraw, ImageFilter
import torch.nn.functional as F
from IPython.display import HTML
import json
2 Datasets¶
Dataset Inspection and Relocation
This Dataset Inspection to Understand file shows how we inspected dataset from given dataset Path location /home/nfs/inf6/data/datasets/Carla_Moritz/SyncAngel3. Then after inspection and some understandings about the files of dataset, we moved dataset to our directory and during this relocation we put conditions to only copy first 6 frames and their relevant files from each sequence folder. To keep the code of current notebook we did not include the code of dataset relocation. This Relocate dataset file contain the code for dataset relocation.
In [2]:
# Define the root dataset path
root_dataset_path = '/home/user/asifa0/Project/CARLABase'
2.1 Transforms¶
In [3]:
transform = transforms.Compose([
transforms.Lambda(lambda image: image.convert('RGB')),
transforms.Resize((256, 512)),
transforms.ToTensor(),
])
In [4]:
def transform_segmentation_target(image):
"""
Transform a segmentation image to a tensor with class labels based on the red channel encoding.
"""
img_array = np.array(image, dtype=np.int64)
label = img_array[:, :, 0]
# Map the red channel to class labels based on the dataset's specifications
label_map = {
0: 0, # Unlabeled
1: 1, # Building
2: 2, # Fence
3: 3, # Other
4: 4, # Pedestrian
5: 5, # Pole
6: 6, # RoadLine
7: 7, # Road
8: 8, # SideWalk
9: 9, # Vegetation
10: 10, # Vehicles
11: 11, # Wall
12: 12, # TrafficSign
13: 13, # Sky
14: 14, # Ground
15: 15, # Bridge
16: 16, # RailTrack
17: 17, # GuardRail
18: 18, # TrafficLight
19: 19, # Static
20: 20, # Dynamic
21: 21, # Water
22: 22, # Terrain
}
label_tensor = torch.from_numpy(np.vectorize(label_map.get)(label))
return label_tensor
In [5]:
target_transform = transforms.Compose([
transforms.Resize((256, 512)),
transforms.Lambda(lambda image: image.convert('RGB')),
transforms.Lambda(lambda img: transform_segmentation_target(img)),
])
In [6]:
depth_transform = transforms.Compose([
transforms.Resize((256, 512)),
transforms.Grayscale(num_output_channels=1),
transforms.ToTensor(),
])
2.2 Functions to Corrupt BASE CARLA¶
In [7]:
def add_gaussian_noise(image):
row, col, ch = image.shape
mean = 0
var = 0.1
sigma = var ** 0.05
gauss = np.random.normal(mean, sigma, (row, col, ch))
gauss = gauss.reshape(row, col, ch)
noisy = image + gauss
noisy = np.clip(noisy, 0, 255)
return noisy.astype(np.uint8)
def add_clutter(image):
draw = ImageDraw.Draw(image)
number_of_shapes = random.randint(1, 5)
for _ in range(number_of_shapes):
x1, y1 = random.randint(0, image.size[0] - 1), random.randint(0, image.size[1] - 1)
x2, y2 = random.randint(0, image.size[0] - 1), random.randint(0, image.size[1] - 1)
draw.line((x1, y1, x2, y2), fill='white', width=2)
return image
def change_illumination(image):
new_image = image.astype(np.float64) * (0.6 + np.random.random() * 0.8)
new_image = np.clip(new_image, 0, 255)
return new_image.astype(np.uint8)
def add_motion_blur(image, angle, radius):
# Apply motion blur to the image using PIL's ImageFilter.
image = image.filter(ImageFilter.GaussianBlur(radius=radius))
return image.rotate(angle, resample=Image.BICUBIC).filter(ImageFilter.GaussianBlur(radius)).rotate(-angle, resample=Image.BICUBIC)
def add_low_light_effect(image, factor):
# Multiply the image with a factor to simulate low light conditions.
new_image = image.astype(np.float64) * factor
new_image = np.clip(new_image, 0, 255)
return new_image.astype(np.uint8)
def add_overexposure_effect(image, factor):
# Add a constant value to simulate overexposure.
new_image = image.astype(np.float64) + factor * 255
new_image = np.clip(new_image, 0, 255)
return new_image.astype(np.uint8)
2.3 Dataset Directory Iteration logic Class¶
In [8]:
class SequentialImagesDataset(Dataset):
def __init__(self, root_dir, transform=None, target_transform=None, depth_transform=None, apply_corruption=False, include_towns=None, exclude_towns=None):
self.root_dir = root_dir
self.transform = transform
self.target_transform = target_transform
self.depth_transform = depth_transform
self.include_towns = include_towns
self.exclude_towns = exclude_towns
self.sequences = self._load_sequences()
self.apply_corruption = apply_corruption
def _load_sequences(self):
sequences = []
for town in sorted(os.listdir(self.root_dir)):
if self.include_towns and town not in self.include_towns:
continue
if self.exclude_towns and town in self.exclude_towns:
continue
town_path = os.path.join(self.root_dir, town)
if os.path.isdir(town_path):
for sequence in sorted(os.listdir(town_path)):
sequence_path = os.path.join(town_path, sequence)
if os.path.isdir(sequence_path):
# Filter files to include only those that match the expected pattern
valid_files = [f for f in os.listdir(sequence_path) if f.startswith('img_') and f.endswith('.png') and any(char.isdigit() for char in f)]
if valid_files:
# Sort the files based on the numeric part of the filename
images = sorted(valid_files, key=lambda f: int(''.join(filter(str.isdigit, f))))
images = [os.path.join(sequence_path, f) for f in images]
sequences.append(images)
return sequences
def __len__(self):
return len(self.sequences)
def __getitem__(self, idx):
image_paths = self.sequences[idx]
optical_flow_paths = [p.replace('img_', 'optical_flow_').replace('.png', '.npy') for p in image_paths]
segmentation_paths = [p.replace('img_', 'segmentation_') for p in image_paths]
depth_paths = [p.replace('img_', 'depth_') for p in image_paths]
sequence_tensors = []
segmentation_tensors = []
depth_tensors = []
optical_flow_tensors = []
# Load input Images and optical_flow files
for image_path, optical_flow_path in zip(image_paths, optical_flow_paths):
try:
img = Image.open(image_path).convert('RGB')
if self.apply_corruption:
img = np.array(img)
img = add_gaussian_noise(img)
# Overexposure effect
factor = random.uniform(0.1, 0.3)
img = add_overexposure_effect(np.array(img), factor)
img = Image.fromarray(img, 'RGB')
#img = add_clutter(img)
# Apply motion blur
angle = random.uniform(0, 0.5)
radius = random.uniform(2, 6)
img = np.array(img)
img = add_motion_blur(Image.fromarray(img), angle, radius)
img = np.array(img)
img = change_illumination(img)
img = Image.fromarray(img, 'RGB')
if self.transform:
img = self.transform(img)
sequence_tensors.append(img)
optical_flow = np.load(optical_flow_path)
optical_flow_tensor = torch.from_numpy(optical_flow).permute(2, 0, 1) # Assuming optical flow has shape (H, W, 2)
optical_flow_tensors.append(optical_flow_tensor)
except Exception as e:
print(f"Error loading image {image_path}: {e}")
sequence_tensors.append(torch.zeros((3, 256, 512)))
optical_flow_tensors.append(torch.zeros((2, 256, 512)))
# Load camera extrinsics for the sequence
meta_path = os.path.join(os.path.dirname(image_paths[0]), 'meta.pkl')
with open(meta_path, 'rb') as f:
meta_data = pickle.load(f)
extrinsics = torch.tensor(meta_data['extrinsics'][:6], dtype=torch.float32)
# Segmented images loading
for seg_path in segmentation_paths:
try:
with Image.open(seg_path) as seg:
if self.target_transform:
seg = self.target_transform(seg)
segmentation_tensors.append(seg)
except Exception as e:
print(f"Error loading segmentation image {seg_path}: {e}")
# Load depth maps
for depth_path in depth_paths:
try:
with Image.open(depth_path) as depth:
if self.depth_transform:
depth = self.depth_transform(depth)
depth_tensors.append(depth)
except Exception as e:
print(f"Error loading depth image {depth_path}: {e}")
sequence_tensor = torch.stack(sequence_tensors) if sequence_tensors else torch.zeros((6, 3, 256, 512))
optical_flow_tensor = torch.stack(optical_flow_tensors) if optical_flow_tensors else torch.zeros((6, 2, 256, 512))
depth_tensor = torch.stack(depth_tensors) if depth_tensors else torch.zeros((6, 1, 256, 512))
segmentation_tensor = torch.stack(segmentation_tensors) if segmentation_tensors else torch.zeros((6, 23, 256, 512))
return sequence_tensor, optical_flow_tensor, segmentation_tensor, extrinsics, depth_tensor
2.4 BASE CARLA¶
In [9]:
# Initialize the dataset for training, validation, and testing
org_train_dataset = SequentialImagesDataset(root_dataset_path, transform=transform, target_transform=target_transform, depth_transform=depth_transform, apply_corruption=False, exclude_towns=['Town02', 'Town10'])
org_val_dataset = SequentialImagesDataset(root_dataset_path, transform=transform, target_transform=target_transform, depth_transform=depth_transform, apply_corruption=False, include_towns=['Town02'])
org_test_dataset = SequentialImagesDataset(root_dataset_path, transform=transform, target_transform=target_transform, depth_transform=depth_transform, apply_corruption=False, include_towns=['Town10'])
2.5 Corrupted CARLA¶
In [10]:
# Initialize the dataset for training, validation, and testing
train_dataset = SequentialImagesDataset(root_dataset_path, transform=transform, target_transform=target_transform, depth_transform=depth_transform, apply_corruption=True, exclude_towns=['Town02', 'Town10'])
val_dataset = SequentialImagesDataset(root_dataset_path, transform=transform, target_transform=target_transform, depth_transform=depth_transform, apply_corruption=True, include_towns=['Town02'])
test_dataset = SequentialImagesDataset(root_dataset_path, transform=transform, target_transform=target_transform, depth_transform=depth_transform, apply_corruption=True, include_towns=['Town10'])
3 Dataloader¶
In [11]:
# DataLoaders for each set
org_train_loader = DataLoader(org_train_dataset, batch_size=1, shuffle=False, num_workers=4)
org_val_loader = DataLoader(org_val_dataset, batch_size=1, shuffle=False, num_workers=4)
org_test_loader = DataLoader(org_test_dataset, batch_size=1, shuffle=False, num_workers=4)
In [12]:
# DataLoaders for each set
train_loader = DataLoader(train_dataset, batch_size=1, shuffle=False, num_workers=4)
val_loader = DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=4)
test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=4)
3.1 Dataset Inspection¶
In [13]:
print( f'Length of Train Loader: {len(train_loader)}')
for sequence_tensor, optical_flow, segmentation_tensor, extrinsics, depth_tensor in train_loader:
print(f'Shape of Input Images: {sequence_tensor.shape}')
print(f'Shape of OpticalFlow tensors: {optical_flow.shape}')
print(f'Shape of Segmented Images: {segmentation_tensor.shape}')
print(f'Shape of extrinsics tensor: {extrinsics.shape}')
print(f'Shape of Depth Images: {depth_tensor.shape}')
break
Length of Train Loader: 8982 Shape of Input Images: torch.Size([1, 6, 3, 256, 512]) Shape of OpticalFlow tensors: torch.Size([1, 6, 2, 512, 1024]) Shape of Segmented Images: torch.Size([1, 6, 256, 512]) Shape of extrinsics tensor: torch.Size([1, 6, 4, 4]) Shape of Depth Images: torch.Size([1, 6, 1, 256, 512])
3.2 Visualising whole sequence of Both Datasets¶
In [14]:
def visualize_sequence(sequence_tensor, rows=2, cols=3):
sequence_length = sequence_tensor.size(0)
# Create subplots
fig, axs = plt.subplots(rows, cols, figsize=(20, 10))
fig.subplots_adjust(hspace=0.1, wspace=0.1)
for i in range(rows * cols):
ax = axs[i // cols, i % cols]
if i < sequence_length:
img_tensor = sequence_tensor[i]
img = np.transpose(img_tensor.numpy(), (1, 2, 0))
ax.imshow(img)
ax.set_title(f"Frame {i+1}", fontsize=10)
ax.axis('off')
plt.tight_layout()
plt.show()
In [15]:
print("Base Carla Images")
for org_batch_idx, (org_sequence_tensors, org_optical_flow, org_segmentation_tensors, org_extrinsics, org_depth_tensor) in enumerate(org_train_loader):
if org_batch_idx == 1:
org_first_sequence_tensor = org_sequence_tensors[0]
if torch.any(org_first_sequence_tensor):
visualize_sequence(org_first_sequence_tensor, rows=2, cols=3)
else:
print(f"The tensor for sequence at index {batch_idx} contains only zeros or is not loaded correctly.")
break
print("Corrupted Carla Images")
for batch_idx, (sequence_tensors, optical_flow, segmentation_tensors, extrinsics, depth_tensor) in enumerate(train_loader):
if batch_idx == 1:
first_sequence_tensor = sequence_tensors[0]
if torch.any(first_sequence_tensor):
visualize_sequence(first_sequence_tensor, rows=2, cols=3)
else:
print(f"The tensor for sequence at index {batch_idx} contains only zeros or is not loaded correctly.")
break
Base Carla Images
Corrupted Carla Images
4 Training Device¶
In [16]:
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(device)
cuda
5 Utils¶
5.1 Segmentation Maps Coloring Function¶
In [17]:
def decode_segmap(label_mask, n_classes=23):
"""
Decode segmentation class labels into a color image.
"""
label_colors = np.array([
(0, 0, 0), # Unlabeled
(70, 70, 70), # Building
(100, 40, 40), # Fence
(55, 90, 80), # others
(220, 20, 60), # Pedestrian
(153, 153, 153), # Pole
(157, 234, 50), # RoadLine
(128, 64, 128), # Road
(244, 35, 232), # SideWalk
(107, 142, 35), # Vegetation
(0, 0, 142), # Vehicles
(102, 102, 156), # Wall
(220, 220, 0), # TrafficSign
(70, 130, 180), # Sky
(81, 0, 81), # Ground
(150, 100, 100), # Bridge
(230, 150, 140), # RailTrack
(180, 165, 180), # GuardRail
(250, 170, 30), # TrafficLight
(110, 190, 160), # Static
(170, 120, 50), # Dynamic
(45, 60, 150), # Water
(145, 170, 100), # Terrain
(255, 255, 255), # ignore_index=22
])
r = np.zeros_like(label_mask).astype(np.uint8)
g = np.zeros_like(label_mask).astype(np.uint8)
b = np.zeros_like(label_mask).astype(np.uint8)
for l in range(0, n_classes):
idx = label_mask == l
r[idx] = label_colors[l, 0]
g[idx] = label_colors[l, 1]
b[idx] = label_colors[l, 2]
rgb = np.stack([r, g, b], axis=2)
return rgb
5.2 Evaluation Metric Functions¶
In [18]:
def calculate_mAcc(preds, targets):
max_preds = preds.argmax(dim=1, keepdim=True)
correct_pred = max_preds.squeeze(1).eq(targets)
acc_per_class = []
for class_idx in range(targets.max() + 1):
correct = correct_pred[targets == class_idx].sum()
total = (targets == class_idx).sum()
if total != 0:
acc_per_class.append(correct.float() / total)
mAcc = torch.mean(torch.stack(acc_per_class)) if acc_per_class else torch.tensor(0)
return mAcc.item()
def calculate_mIoU(predicted_classes, target_classes, num_classes, device='cpu'):
intersection = torch.zeros(num_classes, device=device)
union = torch.zeros(num_classes, device=device)
for class_idx in range(num_classes):
intersection_mask = (predicted_classes == class_idx) & (target_classes == class_idx)
union_mask = (predicted_classes == class_idx) | (target_classes == class_idx)
intersection[class_idx] = intersection_mask.sum()
union[class_idx] = union_mask.sum()
# Avoid division by zero
union[union == 0] = 1
iou = intersection / union
mIoU = iou.mean().item()
return mIoU
5.3 Functions to bring Camera Poses to same scale as ground truth's scale¶
In [19]:
def calculate_distances(poses):
distances = torch.sqrt(torch.sum((poses[0, 1:, :3, 3] - poses[0, :-1, :3, 3]) ** 2, axis=1))
return distances.cpu().detach().numpy()
def analyze_translations(original_poses, predicted_poses):
original_distances = calculate_distances(original_poses)
predicted_distances = calculate_distances(predicted_poses)
statistics = {
"original": {
"mean_distance": np.mean(original_distances),
"std_distance": np.std(original_distances),
"max_distance": np.max(original_distances),
"min_distance": np.min(original_distances),
},
"predicted": {
"mean_distance": np.mean(predicted_distances),
"std_distance": np.std(predicted_distances),
"max_distance": np.max(predicted_distances),
"min_distance": np.min(predicted_distances),
}
}
return statistics
5.4 Function to Visualise all ground truths and generated images For comparison¶
In [20]:
def visualize_sequence_full(sequence_batch, segmentation_targets, predicted_segmentation, depth_tensor, predicted_depth, device):
batch_size, sequence_length, c, h, w = sequence_batch.size()
for i in range(sequence_length):
fig, axes = plt.subplots(1, 5, figsize=(25, 5))
# Input image
axes[0].imshow(sequence_batch[0, i].permute(1, 2, 0).cpu().numpy())
axes[0].set_title('Input Image')
# Ground truth segmentation
gt_segmap = decode_segmap(segmentation_targets[0, i].cpu().numpy())
axes[1].imshow(gt_segmap)
axes[1].set_title('Ground Truth Segmentation')
# Predicted segmentation
pred_segmap = decode_segmap(predicted_segmentation[i].cpu().numpy())
axes[2].imshow(pred_segmap)
axes[2].set_title('Predicted Segmentation')
# Ground truth depth map
axes[3].imshow(depth_tensor[0, i].squeeze().cpu().numpy(), cmap='gray')
axes[3].set_title('Ground Truth Depth Map')
# Predicted depth map
axes[4].imshow(predicted_depth[0, i].squeeze().cpu().detach().numpy(), cmap='gray')
axes[4].set_title('Predicted Depth Map')
plt.show()
5.5 Function to Generate GIFs¶
In [21]:
import imageio
import numpy as np
import torch
import os
def save_gif(image_sequence, filename, duration=10):
with imageio.get_writer(filename, mode='I', duration=duration, loop=0) as writer:
for image in image_sequence:
if image.dtype != np.uint8:
image = (255.0 * image).astype(np.uint8)
writer.append_data(image)
5.6 Function to Plot Camera Poses¶
In [22]:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def plot_camera_trajectories(original_poses, predicted_poses):
original_translations = original_poses[0, :, :3, 3].cpu().detach().numpy()
predicted_translations = predicted_poses[0, :, :3, 3].cpu().detach().numpy()
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
ax.plot(original_translations[:, 0], original_translations[:, 1], original_translations[:, 2],
marker='o', color='red', label='Original Poses')
ax.plot(predicted_translations[:, 0], predicted_translations[:, 1], predicted_translations[:, 2],
marker='^', color='blue', label='Predicted Poses')
for i, t in enumerate(original_translations):
ax.text(t[0], t[1], t[2], f'O{i}', color='red')
for i, t in enumerate(predicted_translations):
ax.text(t[0], t[1], t[2], f'P{i}', color='blue')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_title('Camera Pose Trajectories')
ax.legend()
plt.show()
6 Architecture Components Initialization¶
In [23]:
%run EncoderCNN.ipynb
%run ConvRNN.ipynb
%run Decoder.ipynb
%run MotionEncoder.ipynb
%run CameraEncoder.ipynb
%run RNN.ipynb
%run CameraDecoder.ipynb
%run DepthMap.ipynb
6.1 Models Initializing¶
In [24]:
# Encoder
encoder = Encoder()
print(encoder)
# ConvLSTM parameters
input_channels = 64
hidden_channels = 64
kernel_size = 3
stride = 1
conv_lstm_model = ConvLSTM(in_channels=input_channels,
hidden_channels=hidden_channels,
kernel_size=kernel_size,
stride=stride,)
print(conv_lstm_model)
# Depth Decoder
depth_decoder = DepthDecoder(input_channels=64, depth_channels=1)
print(depth_decoder)
# Decoder Parameters:
num_classes =23
in_channels = 64
out_channels = 23
decoder = Decoder(in_channels, out_channels)
print(decoder)
# Motion Encoder
motion_in_channels = 66
motion_hidden_channels = 64
motion_kernel_size = 3
motion_encoder = MotionEncoder(motion_in_channels, motion_hidden_channels, motion_kernel_size)
print(motion_encoder)
# Camera Encoder
camera_encoder = CameraEncoder(input_channels=64)
print(camera_encoder)
# RNN
hidden_size = 64
output_size = 16
ego_motion_rnn = EgoMotionRNN(hidden_size, output_size)
print(ego_motion_rnn)
# Camera Decoder
camera_decoder = CameraDecoder(input_size=16, hidden_size=36)
print(camera_decoder)
Encoder(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(1): ReLU(inplace=True)
(2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
)
ConvLSTM(
(conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
DepthDecoder(
(upsample1): ConvTranspose2d(64, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(upsample2): ConvTranspose2d(32, 1, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(depth_conv): Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
Decoder(
(up1): Sequential(
(0): Upsample(scale_factor=2.0, mode='bilinear')
(1): DoubleConv(
(double_conv): Sequential(
(0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
)
)
(up2): Sequential(
(0): Upsample(scale_factor=2.0, mode='bilinear')
(1): DoubleConv(
(double_conv): Sequential(
(0): Conv2d(32, 23, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(23, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(23, 23, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(23, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
)
)
)
MotionEncoder(
(conv): Conv2d(66, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
CameraEncoder(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(64, 6, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(adjust_pool): AdaptiveAvgPool2d(output_size=(4, 4))
)
EgoMotionRNN(
(rnn): LSTM(32, 64, batch_first=True)
(fc): Linear(in_features=64, out_features=16, bias=True)
)
CameraDecoder(
(fc1): Linear(in_features=16, out_features=36, bias=True)
(fc2): Linear(in_features=36, out_features=96, bias=True)
(relu): ReLU()
)
6.2 Optimizer¶
In [25]:
# Optimizer
import torch.nn as nn
optimizer = optim.Adam(list(encoder.parameters()) + list(conv_lstm_model.parameters()) + list(depth_decoder.parameters()) + list(decoder.parameters()) + list(motion_encoder.parameters()) + list(camera_encoder.parameters()) + list(ego_motion_rnn.parameters())+ list(camera_decoder.parameters()), lr=3e-4)
6.3 Criterions¶
In [26]:
criterion = nn.CrossEntropyLoss(ignore_index=22)
Cam_criterion = nn.MSELoss()
def Depth_criterian(predicted, true):
min_value = 1e-8
predicted_clamped = torch.clamp(predicted, min=min_value)
true_clamped = torch.clamp(true, min=min_value)
log_predicted = torch.log(predicted_clamped)
log_true = torch.log(true_clamped)
loss = F.l1_loss(log_predicted, log_true)
return loss
In [27]:
torch.cuda.empty_cache()
7 Experiments¶
7.1 End to End Training at Corrupted CARLA¶
7.1.1 Training Loop¶
In [28]:
class SegmentationModelTrainer:
def __init__(self, encoder, conv_lstm, depth_decoder, decoder, motion_encoder, camera_encoder, ego_motion_rnn, camera_decoder, train_loader, val_loader, criterion, Cam_criterion, Depth_criterian, optimizer, device):
self.encoder = encoder
self.conv_lstm_model = conv_lstm
self.depth_decoder = depth_decoder
self.decoder = decoder
self.motion_encoder = motion_encoder
self.camera_encoder = camera_encoder
self.ego_motion_rnn = ego_motion_rnn
self.camera_decoder = camera_decoder
self.train_loader = train_loader
self.val_loader = val_loader
self.criterion = criterion
self.Cam_criterion = Cam_criterion
self.Depth_criterian = Depth_criterian
self.optimizer = optimizer
self.device = device
# Move models to the specified device
self.encoder.to(device)
self.conv_lstm_model.to(device)
self.depth_decoder.to(device)
self.decoder.to(device)
self.motion_encoder.to(device)
self.camera_encoder.to(device)
self.ego_motion_rnn.to(device)
self.camera_decoder.to(device)
def train_epoch(self, epoch):
self.encoder.train()
self.conv_lstm_model.train()
self.depth_decoder.train()
self.decoder.train()
self.motion_encoder.train()
self.camera_encoder.train()
self.ego_motion_rnn.train()
self.camera_decoder.train()
seg_losses = []
depth_losses = []
cam_losses = []
total_losses = []
previous_features = None
for sequence_batch, optical_flow_batch, segmentation_targets, extrinsics, depth_tensor in tqdm(self.train_loader, desc="Training"):
sequence_batch = sequence_batch.to(self.device)
optical_flow_batch = optical_flow_batch.to(self.device)
segmentation_targets = segmentation_targets.to(self.device)
extrinsics = extrinsics.to(self.device)
depth_tensor = depth_tensor.to(self.device)
sequence_length = sequence_batch.size(1)
batch_size = sequence_batch.size(0)
depth_tensor = depth_tensor[0, :, :, :, :]
# Process segmentation targets as needed before using them as model inputs
segmentation_targets = self._process_segmentation_targets(segmentation_targets)
self.optimizer.zero_grad()
encoder_outputs = []
motion_features_list = []
for t in range(sequence_length):
img_t = sequence_batch[:, t, :, :, :]
optical_flow_t = optical_flow_batch[:, t, :, :, :]
# ENCODER
encoder_output_t = self.encoder(img_t)
encoder_outputs.append(encoder_output_t)
# MOTION ENCODER
motion_features_t = self.motion_encoder(encoder_output_t, optical_flow_t)
motion_features_list.append(motion_features_t)
# After processing all time steps, stack the outputs
encoder_output = torch.stack(encoder_outputs, dim=1)
motion_encoder = torch.stack(motion_features_list, dim=1)
# Camera Encoder
motion_encoder = motion_encoder[0, :, :, :, :]
camera_encoder_output_t = self.camera_encoder(motion_encoder)
# RNN
# Doing dimentionality reduction
camera_encoder_output_t = camera_encoder_output_t.mean(dim=0, keepdim=True)
camera_encoder_output_flat = camera_encoder_output_t.view(batch_size, sequence_length, -1)
extrinsics_flat = extrinsics.view(batch_size, sequence_length, -1)
combined_input = torch.cat((camera_encoder_output_flat, extrinsics_flat), dim=2)
ego_motion_output = self.ego_motion_rnn(combined_input)
# CAMERA DECODER
ego_motion_output = ego_motion_output[:, -1, :]
camera_poses = self.camera_decoder(ego_motion_output)
# Calculate the scaling factor based on the means of the original and predicted distances
stats = analyze_translations(extrinsics, camera_poses)
scaling_factor = stats['original']['mean_distance'] / stats['predicted']['mean_distance']
# Apply the scaling factor to the translations of the predicted camera poses
camera_poses_scaled = camera_poses.clone()
camera_poses_scaled[:, :, :3, 3] *= scaling_factor
# Hidden Layer_org
batch_size, seq_length, channels, height, width = encoder_output.size()
hidden_state, cell_state = self.conv_lstm_model.init_hidden(batch_size, height, width)
# ConvLSTM
conv_lstm_output, (hidden_state, cell_state) = self.conv_lstm_model(encoder_output, (hidden_state.to(self.device), cell_state.to(self.device)))
batch_size, seq_length, channels, height, width = conv_lstm_output.size()
conv_lstm_output = conv_lstm_output.view(batch_size * seq_length, channels, height, width)
# DEPTH DECODER
depth_decoder_output = self.depth_decoder(conv_lstm_output)
# Decoder
decoder_output = self.decoder(conv_lstm_output)
# Backpropagation for Segmentation Loss
seg_loss = self.criterion(decoder_output, segmentation_targets)
seg_losses.append(seg_loss.item())
# Backpropagation for Depth Loss
depth_loss = self.Depth_criterian(depth_decoder_output, depth_tensor)
depth_losses.append(depth_loss.item())
# Backpropagation for Camera Pose Loss
cam_loss = self.Cam_criterion(camera_poses_scaled, extrinsics)
cam_losses.append(cam_loss.item())
# Total Losses
total_loss = seg_loss + depth_loss + cam_loss
self.optimizer.zero_grad()
total_loss.backward()
self.optimizer.step()
total_losses.append(total_loss.item())
#writer.add_scalar('Loss/Train/Segmentation', seg_loss.item(), global_step=epoch * len(self.train_loader) + batch_idx)
#writer.add_scalar('Loss/Train/Depth', depth_loss.item(), global_step=epoch * len(self.train_loader) + batch_idx)
#writer.add_scalar('Loss/Train/Camera', cam_loss.item(), global_step=epoch * len(self.train_loader) + batch_idx)
# Save all models here
model_save_path = 'Models/Corrupted'
if not os.path.exists(model_save_path):
os.makedirs(model_save_path)
torch.save(self.encoder.state_dict(), os.path.join(model_save_path, f'Encoder.pth'))
torch.save(self.conv_lstm_model.state_dict(), os.path.join(model_save_path, f'ConvLSTM.pth'))
torch.save(self.depth_decoder.state_dict(), os.path.join(model_save_path, f'DepthDecoder.pth'))
torch.save(self.decoder.state_dict(), os.path.join(model_save_path, f'SegmentationDecoder.pth'))
torch.save(self.motion_encoder.state_dict(), os.path.join(model_save_path, f'MotionEncoder.pth'))
torch.save(self.camera_encoder.state_dict(), os.path.join(model_save_path, f'CameraEncoder.pth'))
torch.save(self.ego_motion_rnn.state_dict(), os.path.join(model_save_path, f'EgoMotionRNN.pth'))
torch.save(self.camera_decoder.state_dict(), os.path.join(model_save_path, f'CameraDecoder.pth'))
# converting lists to tensors to calculate means
seg_losses_tensor = torch.tensor(seg_losses)
depth_losses_tensor = torch.tensor(depth_losses)
cam_losses_tensor = torch.tensor(cam_losses)
total_losses_tensor = torch.tensor(total_losses)
# Calculating means
mean_seg_loss = torch.mean(seg_losses_tensor).item()
mean_depth_loss = torch.mean(depth_losses_tensor).item()
mean_cam_loss = torch.mean(cam_losses_tensor).item()
mean_total_loss = torch.mean(total_losses_tensor).item()
return mean_seg_loss, mean_depth_loss, mean_cam_loss, mean_total_loss
@torch.no_grad()
def validate_epoch(self, epoch):
self.encoder.eval()
self.conv_lstm_model.eval()
self.depth_decoder.eval()
self.decoder.eval()
self.motion_encoder.eval()
self.camera_encoder.eval()
self.ego_motion_rnn.eval()
self.camera_decoder.eval()
seg_val_losses = []
cam_val_losses = []
depth_val_losses = []
val_total_losses = []
mAccs = []
val_mIoUs = []
previous_features = None
for val_sequence_batch, val_optical_flow_batch, val_segmentation_targets, val_extrinsics, val_depth_tensor in tqdm(self.val_loader, desc="Validation"):
val_sequence_batch = val_sequence_batch.to(self.device)
val_segmentation_targets = val_segmentation_targets.to(self.device)
val_optical_flow_batch = val_optical_flow_batch.to(self.device)
val_extrinsics = val_extrinsics.to(self.device)
val_depth_tensor = val_depth_tensor.to(self.device)
# Flatten the sequence and batch dimensions
sequence_length = val_sequence_batch.size(1)
batch_size = val_sequence_batch.size(0)
val_depth_tensor = val_depth_tensor[0, :, :, :, :]
# Process segmentation targets as needed before using them as model inputs
val_segmentation_targets = self._process_segmentation_targets(val_segmentation_targets)
encoder_outputs = []
motion_features_list = []
for t in range(sequence_length):
img_t = val_sequence_batch[:, t, :, :, :]
optical_flow_t = val_optical_flow_batch[:, t, :, :, :]
# ENCODER
encoder_output_t = self.encoder(img_t)
encoder_outputs.append(encoder_output_t)
# MOTION ENCODER
motion_features_t = self.motion_encoder(encoder_output_t, optical_flow_t)
motion_features_list.append(motion_features_t)
# After processing all time steps, stack the outputs
encoder_output = torch.stack(encoder_outputs, dim=1)
motion_encoder = torch.stack(motion_features_list, dim=1)
# Camera Encoder
motion_encoder = motion_encoder[0, :, :, :, :]
camera_encoder_output_t = self.camera_encoder(motion_encoder)
# RNN
# Doing dimentionality reduction
camera_encoder_output_t = camera_encoder_output_t.mean(dim=0, keepdim=True)
camera_encoder_output_flat = camera_encoder_output_t.view(batch_size, sequence_length, -1)
extrinsics_flat = val_extrinsics.view(batch_size, sequence_length, -1)
combined_input = torch.cat((camera_encoder_output_flat, extrinsics_flat), dim=2)
ego_motion_output = self.ego_motion_rnn(combined_input)
# CAMERA DECODER
ego_motion_output = ego_motion_output[:, -1, :]
camera_poses = self.camera_decoder(ego_motion_output)
#camera_poses = camera_poses.view(-1, 4, 4)
# Calculate the scaling factor based on the means of the original and predicted distances
stats = analyze_translations(val_extrinsics, camera_poses)
scaling_factor = stats['original']['mean_distance'] / stats['predicted']['mean_distance']
# Apply the scaling factor to the translations of the predicted camera poses
camera_poses_scaled = camera_poses.clone()
camera_poses_scaled[:, :, :3, 3] *= scaling_factor
# Hidden Layer_org
batch_size, seq_length, channels, height, width = encoder_output.size()
hidden_state, cell_state = self.conv_lstm_model.init_hidden(batch_size, height, width)
# ConvLSTM
conv_lstm_output, (hidden_state, cell_state) = self.conv_lstm_model(encoder_output, (hidden_state.to(self.device), cell_state.to(self.device)))
batch_size, seq_length, channels, height, width = conv_lstm_output.size()
conv_lstm_output = conv_lstm_output.view(batch_size * seq_length, channels, height, width)
# DEPTH DECODER
depth_decoder_output = self.depth_decoder(conv_lstm_output)
# Decoder
decoder_output = self.decoder(conv_lstm_output)
mAcc = calculate_mAcc(decoder_output, val_segmentation_targets)
mAccs.append(mAcc)
predicted_classes = torch.argmax(decoder_output, dim=1)
mIoU = calculate_mIoU(predicted_classes, val_segmentation_targets, num_classes, self.device)
val_mIoUs.append(mIoU)
seg_val_loss = self.criterion(decoder_output, val_segmentation_targets)
seg_val_losses.append(seg_val_loss.item())
depth_val_loss = self.Depth_criterian(depth_decoder_output, val_depth_tensor)
depth_val_losses.append(depth_val_loss.item())
cam_val_loss = self.Cam_criterion(camera_poses_scaled, val_extrinsics)
cam_val_losses.append(cam_val_loss.item())
val_total_loss = seg_val_loss + depth_val_loss + cam_val_loss
val_total_losses.append(val_total_loss.item())
# After the loop, convert lists to tensors to calculate means
seg_val_losses_tensor = torch.tensor(seg_val_losses)
depth_val_losses_tensor = torch.tensor(depth_val_losses)
cam_val_losses_tensor = torch.tensor(cam_val_losses)
mAccs_tensor = torch.tensor(mAccs)
val_mIoUs_tensor = torch.tensor(val_mIoUs)
val_total_losses_tensor = torch.tensor(val_total_losses)
# Calculate means
mean_seg_val_loss = torch.mean(seg_val_losses_tensor).item()
mean_depth_val_loss = torch.mean(depth_val_losses_tensor).item()
mean_cam_val_loss = torch.mean(cam_val_losses_tensor).item()
mean_mAcc = torch.mean(mAccs_tensor).item()
mean_val_mIoU = torch.mean(val_mIoUs_tensor).item()
mean_val_total_loss = torch.mean(val_total_losses_tensor).item()
return mean_seg_val_loss, mean_depth_val_loss, mean_cam_val_loss, mean_mAcc, mean_val_mIoU, mean_val_total_loss
def fit(self, epochs):
for epoch in range(epochs):
print(f"Epoch {epoch+1}/{epochs}")
mean_seg_losses, mean_depth_losses, mean_cam_losses, mean_total_loss= self.train_epoch(epochs)
seg_loss_list.append(mean_seg_losses)
depth_loss_list.append(mean_depth_losses)
cam_loss_list.append(mean_cam_losses)
total_loss_list.append(mean_total_loss)
print(f"Epoch {epoch} - Segmentation Loss: {mean_seg_losses}, Depth Loss: {mean_depth_losses}, Camera Pose Loss: {mean_cam_losses}")
mean_seg_val_loss, mean_depth_val_loss, mean_cam_val_loss, mean_mAcc, mean_val_mIoU, mean_val_total_loss= trainer.validate_epoch(epochs)
seg_val_loss_list.append(mean_seg_val_loss)
depth_val_loss_list.append(mean_depth_val_loss)
cam_val_loss_list.append(mean_cam_val_loss)
mAcc_list.append(mean_mAcc)
val_mIoU_list.append(mean_val_mIoU)
val_total_loss_list.append(mean_val_total_loss)
print(f"Epoch {epoch} - Val Segmentation Loss: {mean_seg_val_loss}, Val Depth Loss: {mean_depth_val_loss}, Val Camera Pose Loss: {mean_cam_val_loss}")
print(f"Epoch {epoch} - Val mAcc: {mean_mAcc}, Val mIoU: {mean_val_mIoU}")
def forward_pass(self, sequence_batch, optical_flow_batch, extrinsics):
"""Perform a forward pass on the model to get the predictions for visualization purposes."""
self.encoder.eval()
self.conv_lstm_model.eval()
self.depth_decoder.eval()
self.decoder.eval()
self.motion_encoder.eval()
self.camera_encoder.eval()
self.ego_motion_rnn.eval()
self.camera_decoder.eval()
with torch.no_grad():
sequence_batch = sequence_batch.to(self.device)
optical_flow_batch = optical_flow_batch.to(self.device)
extrinsics = extrinsics.to(self.device)
sequence_length = sequence_batch.size(1)
batch_size = sequence_batch.size(0)
encoder_outputs = []
motion_features_list = []
for t in range(sequence_length):
img_t = sequence_batch[:, t, :, :, :]
optical_flow_t = optical_flow_batch[:, t, :, :, :]
encoder_output_t = self.encoder(img_t)
encoder_outputs.append(encoder_output_t)
motion_features_t = self.motion_encoder(encoder_output_t, optical_flow_t)
motion_features_list.append(motion_features_t)
encoder_output = torch.stack(encoder_outputs, dim=1)
motion_encoder_output = torch.stack(motion_features_list, dim=1)
# Process for camera poses
# Camera Encoder
motion_encoder_output = motion_encoder_output.mean(dim=1)
camera_encoder_output_t = self.camera_encoder(motion_encoder_output)
# RNN
# Doing dimentionality reduction
camera_encoder_output_t = camera_encoder_output_t.mean(dim=0, keepdim=True)
camera_encoder_output_flat = camera_encoder_output_t.view(batch_size, sequence_length, -1)
extrinsics_flat = extrinsics.view(batch_size, sequence_length, -1)
combined_input = torch.cat((camera_encoder_output_flat, extrinsics_flat), dim=2)
ego_motion_output = self.ego_motion_rnn(combined_input)
ego_motion_output = ego_motion_output[:, -1, :]
camera_posess = self.camera_decoder(ego_motion_output)
# Calculate the scaling factor based on the means of the original and predicted distances
stats = analyze_translations(extrinsics, camera_posess)
scaling_factor = stats['original']['mean_distance'] / stats['predicted']['mean_distance']
# Apply the scaling factor to the translations of the predicted camera poses
camera_poses_scaled = camera_posess.clone()
camera_poses_scaled[:, :, :3, 3] *= scaling_factor
# Process for depth and segmentation
# Hidden Layer_org
batch_size, seq_length, channels, height, width = encoder_output.size()
hidden_state, cell_state = self.conv_lstm_model.init_hidden(batch_size, height, width)
# ConvLSTM
conv_lstm_output, (hidden_state, cell_state) = self.conv_lstm_model(encoder_output, (hidden_state.to(self.device), cell_state.to(self.device)))
batch_size, seq_length, channels, height, width = conv_lstm_output.size()
conv_lstm_output = conv_lstm_output.view(batch_size * seq_length, channels, height, width)
depth_decoder_output = self.depth_decoder(conv_lstm_output)
decoder_output = self.decoder(conv_lstm_output)
predicted_segmentation = torch.argmax(decoder_output, dim=1)
seq, chan, h, w = depth_decoder_output.size()
depth_decoder_output = depth_decoder_output.view(batch_size, seq, h, w)
# Return the necessary outputs
return predicted_segmentation, depth_decoder_output, camera_poses_scaled
def _process_segmentation_targets(self, segmentation_targets):
# Preprocessing on segmentation_targets
if segmentation_targets.size()[-1] == 3:
batch_size, seq_len, _, height, width = segmentation_targets.size()
segmentation_targets= segmentation_targets[0, :, :, :, 0]
segmentation_targets = segmentation_targets.long()
elif segmentation_targets.dim() == 4:
segmentation_targets= segmentation_targets[0, :, :, :]
else:
batch_size, seq_len, _, height, width = segmentation_targets.size()
segmentation_targets= segmentation_targets[0, :, 0, :, :]
segmentation_targets = segmentation_targets.long()
return segmentation_targets
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
#device = torch.device('cpu')
# Initialize the Trainer
trainer = SegmentationModelTrainer(encoder, conv_lstm_model, depth_decoder, decoder, motion_encoder, camera_encoder, ego_motion_rnn, camera_decoder, train_loader, val_loader, criterion, Cam_criterion, Depth_criterian, optimizer, device)
# Train the model
val_mIoU_list = []
mAcc_list = []
seg_loss_list, depth_loss_list, cam_loss_list, total_loss_list = [], [], [], []
seg_val_loss_list, depth_val_loss_list, cam_val_loss_list, val_total_loss_list= [], [], [], []
epochs=5
trainer.fit(epochs)
writer.close()
Epoch 1/5
Training: 100%|███████████████████████████████████████████████████████████████████| 8982/8982 [3:14:36<00:00, 1.30s/it] Validation: 100%|███████████████████████████████████████████████████████████████████| 1497/1497 [40:42<00:00, 1.63s/it]
Epoch 0 - Val Segmentation Loss: 2.589580774307251, Val Depth Loss: 17.63500213623047, Val Camera Pose Loss: 3286.7666015625 Epoch 0 - Val mAcc: 0.10046735405921936, Val mIoU: 0.048291344195604324 Epoch 2/5
Training: 100%|███████████████████████████████████████████████████████████████████| 8982/8982 [3:55:11<00:00, 1.57s/it] Validation: 100%|███████████████████████████████████████████████████████████████████| 1497/1497 [36:12<00:00, 1.45s/it]
Epoch 1 - Val Segmentation Loss: 2.100627899169922, Val Depth Loss: 17.63500213623047, Val Camera Pose Loss: 2323.039794921875 Epoch 1 - Val mAcc: 0.11038091778755188, Val mIoU: 0.05654735118150711 Epoch 3/5
Training: 100%|███████████████████████████████████████████████████████████████████| 8982/8982 [1:52:59<00:00, 1.32it/s] Validation: 100%|███████████████████████████████████████████████████████████████████| 1497/1497 [21:19<00:00, 1.17it/s]
Epoch 2 - Val Segmentation Loss: 2.0565876960754395, Val Depth Loss: 17.63500213623047, Val Camera Pose Loss: 3703.7783203125 Epoch 2 - Val mAcc: 0.11377152055501938, Val mIoU: 0.05857199430465698 Epoch 4/5
Training: 100%|███████████████████████████████████████████████████████████████████| 8982/8982 [2:07:34<00:00, 1.17it/s] Validation: 100%|███████████████████████████████████████████████████████████████████| 1497/1497 [20:44<00:00, 1.20it/s]
Epoch 3 - Val Segmentation Loss: 2.1129634380340576, Val Depth Loss: 17.63500213623047, Val Camera Pose Loss: 3345.041015625 Epoch 3 - Val mAcc: 0.10820959508419037, Val mIoU: 0.0562012605369091 Epoch 5/5
Training: 100%|███████████████████████████████████████████████████████████████████| 8982/8982 [2:10:44<00:00, 1.15it/s] Validation: 100%|███████████████████████████████████████████████████████████████████| 1497/1497 [28:54<00:00, 1.16s/it]
Epoch 4 - Val Segmentation Loss: 2.017798662185669, Val Depth Loss: 8.998672485351562, Val Camera Pose Loss: 3647.602783203125 Epoch 4 - Val mAcc: 0.11395377665758133, Val mIoU: 0.06037492677569389
--------------------------------------------------------------------------- NameError Traceback (most recent call last) Cell In[28], line 362 360 epochs=5 361 trainer.fit(epochs) --> 362 writer.close() NameError: name 'writer' is not defined
7.1.2 Results¶
7.1.2.1 Save Stats¶
In [29]:
training_stats = {
"seg_loss_list": seg_loss_list,
"depth_loss_list": depth_loss_list,
"cam_loss_list": cam_loss_list,
"total_loss_list": total_loss_list,
"seg_val_loss_list": seg_val_loss_list,
"depth_val_loss_list": depth_val_loss_list,
"cam_val_loss_list": cam_val_loss_list,
"val_total_loss_list": val_total_loss_list,
"mAcc_list": mAcc_list,
"val_mIoU_list": val_mIoU_list,
}
with open('Models/Corrupted/training_stats.json', 'w') as f:
json.dump(training_stats, f, indent=4)
print("Training statistics saved to 'Models/Corrupted/training_stats.json'.")
Training statistics saved to 'Models/Corrupted/training_stats.json'.
7.1.2.2 Output Images¶
In [66]:
def load_model_components(trainer, model_save_path):
trainer.encoder.load_state_dict(torch.load(os.path.join(model_save_path, 'Encoder.pth'), map_location=trainer.device))
trainer.conv_lstm_model.load_state_dict(torch.load(os.path.join(model_save_path, 'ConvLSTM.pth'), map_location=trainer.device))
trainer.depth_decoder.load_state_dict(torch.load(os.path.join(model_save_path, 'DepthDecoder.pth'), map_location=trainer.device))
trainer.decoder.load_state_dict(torch.load(os.path.join(model_save_path, 'SegmentationDecoder.pth'), map_location=trainer.device))
trainer.motion_encoder.load_state_dict(torch.load(os.path.join(model_save_path, 'MotionEncoder.pth'), map_location=trainer.device))
trainer.camera_encoder.load_state_dict(torch.load(os.path.join(model_save_path, 'CameraEncoder.pth'), map_location=trainer.device))
trainer.ego_motion_rnn.load_state_dict(torch.load(os.path.join(model_save_path, 'EgoMotionRNN.pth'), map_location=trainer.device))
trainer.camera_decoder.load_state_dict(torch.load(os.path.join(model_save_path, 'CameraDecoder.pth'), map_location=trainer.device))
GIF_path = "GIF/CorruptedCARLA"
def visualize_predictions(trainer, data_loader, model_save_path):
# Load model components
load_model_components(trainer, model_save_path)
# Visualization loop
for batch in tqdm(data_loader, desc="Visualization"):
sequence_batch, optical_flow_batch, segmentation_targets, extrinsics, depth_tensor = batch
predicted_segmentation, predicted_depth, camera_poses = trainer.forward_pass(sequence_batch, optical_flow_batch, extrinsics)
# Visualize predictions
visualize_sequence_full(
sequence_batch.cpu(),
segmentation_targets.cpu(),
predicted_segmentation.cpu(),
depth_tensor.cpu(),
predicted_depth.cpu(),
trainer.device
)
plot_camera_trajectories(extrinsics.cpu(), camera_poses.cpu())
# GIFs Saving
input_images_sequence = []
gt_segmentation_sequence = []
predicted_segmentation_sequence = []
gt_depth_maps_sequence = []
predicted_depth_maps_sequence = []
batch_size, sequence_length, c, h, w = sequence_batch.size()
for i in range(sequence_length):
input_images_sequence.append(sequence_batch[0, i].permute(1, 2, 0).cpu().numpy())
gt_segmap = decode_segmap(segmentation_targets[0, i].cpu().numpy())
gt_segmentation_sequence.append(gt_segmap)
pred_segmap = decode_segmap(predicted_segmentation[i].cpu().numpy())
predicted_segmentation_sequence.append(pred_segmap)
gt_depth_map = depth_tensor[0, i].squeeze().cpu().numpy()
if np.max(gt_depth_map) > 255 or np.min(gt_depth_map) < 0:
gt_depth_map = (gt_depth_map - np.min(gt_depth_map)) / (np.max(gt_depth_map) - np.min(gt_depth_map)) # Normalizing to 0-1
gt_depth_maps_sequence.append(gt_depth_map)
predicted_depth_map = predicted_depth[0, i].squeeze().cpu().detach().numpy()
if np.max(predicted_depth_map) > 255 or np.min(predicted_depth_map) < 0:
predicted_depth_map = (predicted_depth_map - np.min(predicted_depth_map)) / (np.max(predicted_depth_map) - np.min(predicted_depth_map)) # Normalizing to 0-1
predicted_depth_maps_sequence.append(predicted_depth_map)
save_gif(input_images_sequence, os.path.join(GIF_path, 'input_images.gif'))
save_gif(gt_segmentation_sequence, os.path.join(GIF_path, 'gt_segmentation.gif'))
save_gif(predicted_segmentation_sequence, os.path.join(GIF_path, 'predicted_segmentation.gif'))
save_gif(gt_depth_maps_sequence, os.path.join(GIF_path, 'gt_depth_maps.gif'), duration=0.2)
save_gif(predicted_depth_maps_sequence, os.path.join(GIF_path, 'predicted_depth_maps.gif'), duration=0.2)
break
model_save_path = 'Models/Corrupted'
visualize_predictions(trainer, trainer.val_loader, model_save_path)
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Visualization: 0%| | 0/8982 [00:17<?, ?it/s]
7.1.2.3 GIFs¶
In [32]:
from IPython.display import HTML, display
import os
GIF_path = "GIF/CorruptedCARLA"
def Show_gifs(path):
gif_filenames = ['input_images.gif', 'gt_segmentation.gif', 'predicted_segmentation.gif', 'gt_depth_maps.gif', 'predicted_depth_maps.gif']
gif_html = '<table><tr>'
for gif in gif_filenames:
gif_path = os.path.join(path, gif)
gif_html += f'<td><img src="{gif_path}" alt="{gif}" style="width:160px;height:120px;"></td>'
gif_html += '</tr></table>'
return HTML(gif_html)
display(Show_gifs(GIF_path))
 |  |  |  |  |
7.1.3 Training, Validation, Test Statistics¶
In [41]:
import matplotlib.pyplot as plt
import json
with open('Models/Corrupted/training_stats.json', 'r') as f:
training_stats = json.load(f)
epochs = range(1, len(training_stats['seg_loss_list']) + 1)
plt.figure(figsize=(14, 10))
# Mean Accuracy (mAcc)
plt.subplot(2, 3, 1)
plt.plot(epochs, training_stats['mAcc_list'], label='Mean Accuracy (mAcc)')
plt.plot(epochs, training_stats['val_mIoU_list'], label='Mean IoU (mIoU)', linestyle='--')
plt.xlabel('Epoch')
plt.ylabel('mAcc')
plt.title('Mean Accuracy Over Epochs')
plt.legend()
# Segmentation Losses
#plt.figure(figsize=(14, 10))
plt.subplot(2, 3, 2)
plt.plot(epochs, training_stats['seg_loss_list'], label='Segmentation Loss')
plt.plot(epochs, training_stats['seg_val_loss_list'], label='Validation Segmentation Loss', linestyle='--')
plt.title('Segmentation Losses')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
# Depth Losses
plt.subplot(2, 3, 3)
plt.plot(epochs, training_stats['depth_loss_list'], label='Depth Loss')
plt.plot(epochs, training_stats['depth_val_loss_list'], label='Validation Depth Loss', linestyle='--')
plt.title('Depth Losses')
plt.xlabel('Epoch')
plt.legend()
# Camera Pose Losses
plt.subplot(2, 3, 4)
plt.plot(epochs, training_stats['cam_loss_list'], label='Camera Pose Loss')
plt.plot(epochs, training_stats['cam_val_loss_list'], label='Validation Camera Pose Loss', linestyle='--')
plt.title('Camera Pose Losses')
plt.xlabel('Epoch')
plt.legend()
# Combined Model Losses
plt.subplot(2, 3, 5)
plt.plot(epochs, training_stats['total_loss_list'], label='Combined Model Loss')
plt.plot(epochs, training_stats['val_total_loss_list'], label='Validation Combined Model Loss', linestyle='--')
plt.title('Overall Model Losses')
plt.xlabel('Epoch')
plt.legend()
plt.tight_layout()
plt.show()
7.2 End to End Training at Base CARLA¶
7.2.1 Results¶
7.2.1.1 Reloading Saved Models¶
In [75]:
def load_model_components(trainer, model_save_path):
trainer.encoder.load_state_dict(torch.load(os.path.join(model_save_path, 'Encoder.pth'), map_location=trainer.device))
trainer.conv_lstm_model.load_state_dict(torch.load(os.path.join(model_save_path, 'ConvLSTM.pth'), map_location=trainer.device))
trainer.depth_decoder.load_state_dict(torch.load(os.path.join(model_save_path, 'DepthDecoder.pth'), map_location=trainer.device))
trainer.decoder.load_state_dict(torch.load(os.path.join(model_save_path, 'SegmentationDecoder.pth'), map_location=trainer.device))
trainer.motion_encoder.load_state_dict(torch.load(os.path.join(model_save_path, 'MotionEncoder.pth'), map_location=trainer.device))
trainer.camera_encoder.load_state_dict(torch.load(os.path.join(model_save_path, 'CameraEncoder.pth'), map_location=trainer.device))
trainer.ego_motion_rnn.load_state_dict(torch.load(os.path.join(model_save_path, 'EgoMotionRNN.pth'), map_location=trainer.device))
trainer.camera_decoder.load_state_dict(torch.load(os.path.join(model_save_path, 'CameraDecoder.pth'), map_location=trainer.device))
7.2.1.2 Output Images¶
In [79]:
GIF_path = "GIF/CorruptedCARLA"
def visualize_predictions(trainer, data_loader, model_save_path):
load_model_components(trainer, model_save_path)
for batch in tqdm(data_loader, desc="Visualization"):
sequence_batch, optical_flow_batch, segmentation_targets, extrinsics, depth_tensor = batch
predicted_segmentation, predicted_depth, camera_poses = trainer.forward_pass(sequence_batch, optical_flow_batch, extrinsics)
visualize_sequence_full(
sequence_batch.cpu(),
segmentation_targets.cpu(),
predicted_segmentation.cpu(),
depth_tensor.cpu(),
predicted_depth.cpu(),
trainer.device
)
plot_camera_trajectories(extrinsics.cpu(), camera_poses.cpu())
# GIFs Saving
input_images_sequence = []
gt_segmentation_sequence = []
predicted_segmentation_sequence = []
gt_depth_maps_sequence = []
predicted_depth_maps_sequence = []
batch_size, sequence_length, c, h, w = sequence_batch.size()
for i in range(sequence_length):
input_images_sequence.append(sequence_batch[0, i].permute(1, 2, 0).cpu().numpy())
gt_segmap = decode_segmap(segmentation_targets[0, i].cpu().numpy())
gt_segmentation_sequence.append(gt_segmap)
pred_segmap = decode_segmap(predicted_segmentation[i].cpu().numpy())
predicted_segmentation_sequence.append(pred_segmap)
gt_depth_map = depth_tensor[0, i].squeeze().cpu().numpy()
if np.max(gt_depth_map) > 255 or np.min(gt_depth_map) < 0:
gt_depth_map = (gt_depth_map - np.min(gt_depth_map)) / (np.max(gt_depth_map) - np.min(gt_depth_map)) # Normalizing to 0-1
gt_depth_maps_sequence.append(gt_depth_map)
predicted_depth_map = predicted_depth[0, i].squeeze().cpu().detach().numpy()
if np.max(predicted_depth_map) > 255 or np.min(predicted_depth_map) < 0:
predicted_depth_map = (predicted_depth_map - np.min(predicted_depth_map)) / (np.max(predicted_depth_map) - np.min(predicted_depth_map)) # Normalizing to 0-1
predicted_depth_maps_sequence.append(predicted_depth_map)
save_gif(input_images_sequence, os.path.join(GIF_path, 'input_images.gif'))
save_gif(gt_segmentation_sequence, os.path.join(GIF_path, 'gt_segmentation.gif'))
save_gif(predicted_segmentation_sequence, os.path.join(GIF_path, 'predicted_segmentation.gif'))
save_gif(gt_depth_maps_sequence, os.path.join(GIF_path, 'gt_depth_maps.gif'), duration=0.2)
save_gif(predicted_depth_maps_sequence, os.path.join(GIF_path, 'predicted_depth_maps.gif'), duration=0.2)
break
model_save_path = 'Models/BaseCARLA'
visualize_predictions(trainer, org_val_loader, model_save_path)
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7.2.1.3 GIFs¶
In [73]:
GIF_path = "GIF/BaseCARLA"
display(Show_gifs(GIF_path))
 |  |  |  |  |
7.2.2 Training, Validation, Test Statistics¶
In [74]:
import matplotlib.pyplot as plt
import json
with open('Models/BaseCARLA/training_stats.json', 'r') as f:
training_stats = json.load(f)
epochs = range(1, len(training_stats['seg_loss_list']) + 1)
plt.figure(figsize=(14, 10))
# Mean Accuracy (mAcc)
plt.subplot(2, 3, 1)
plt.plot(epochs, training_stats['mAcc_list'], label='Mean Accuracy (mAcc)')
plt.plot(epochs, training_stats['val_mIoU_list'], label='Mean IoU (mIoU)', linestyle='--')
plt.xlabel('Epoch')
plt.ylabel('mAcc')
plt.title('Mean Accuracy Over Epochs')
plt.legend()
# Segmentation Losses
#plt.figure(figsize=(14, 10))
plt.subplot(2, 3, 2)
plt.plot(epochs, training_stats['seg_loss_list'], label='Segmentation Loss')
plt.plot(epochs, training_stats['seg_val_loss_list'], label='Validation Segmentation Loss', linestyle='--')
plt.title('Segmentation Losses')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
# Depth Losses
plt.subplot(2, 3, 3)
plt.plot(epochs, training_stats['depth_loss_list'], label='Depth Loss')
plt.plot(epochs, training_stats['depth_val_loss_list'], label='Validation Depth Loss', linestyle='--')
plt.title('Depth Losses')
plt.xlabel('Epoch')
plt.legend()
# Camera Pose Losses
plt.subplot(2, 3, 4)
plt.plot(epochs, training_stats['cam_loss_list'], label='Camera Pose Loss')
plt.plot(epochs, training_stats['cam_val_loss_list'], label='Validation Camera Pose Loss', linestyle='--')
plt.title('Camera Pose Losses')
plt.xlabel('Epoch')
plt.legend()
# Combined Model Losses
plt.subplot(2, 3, 5)
plt.plot(epochs, training_stats['total_loss_list'], label='Combined Model Loss')
plt.plot(epochs, training_stats['val_total_loss_list'], label='Validation Combined Model Loss', linestyle='--')
plt.title('Overall Model Losses')
plt.xlabel('Epoch')
plt.legend()
plt.tight_layout()
plt.show()
7.3 No Filters Model¶
7.3.1 Results¶
7.3.1.1 Reloading Saved Models¶
In [29]:
pretrained_weights_path = 'Models/NoFilter/pretrained_weights.pth'
fine_tuned_weights_path = 'Models/NoFilter/fine_tuned_weights.pth'
GIF_path1 = "GIF/NoFilter/Pretrained"
GIF_path2 = "GIF/NoFilter/Finetuned"
7.3.1.2 Output Images¶
In [31]:
trainer = SegmentationModelTrainer(encoder, conv_lstm_model, depth_decoder, decoder, motion_encoder, camera_encoder, ego_motion_rnn, camera_decoder, org_train_loader, org_val_loader, train_loader, val_loader, criterion, Cam_criterion, Depth_criterian, optimizer, device)
def load_weights(model_trainer, weights_path, mode):
state = torch.load(weights_path, map_location=device)
model_trainer.encoder.load_state_dict(state['encoder_state_dict'])
model_trainer.depth_decoder.load_state_dict(state['depth_decoder_state_dict'])
model_trainer.decoder.load_state_dict(state['decoder_state_dict'])
model_trainer.motion_encoder.load_state_dict(state['motion_encoder_state_dict'])
model_trainer.camera_encoder.load_state_dict(state['camera_encoder_state_dict'])
model_trainer.camera_decoder.load_state_dict(state['camera_decoder_state_dict'])
if mode == "fine_tuning":
model_trainer.conv_lstm_model.load_state_dict(state['conv_lstm_state_dict'])
model_trainer.ego_motion_rnn.load_state_dict(state['ego_motion_rnn_state_dict'])
def visualize_predictions(model_trainer, weights_path, data_loader, mode):
load_weights(model_trainer, weights_path, mode)
model_trainer.switch_dataloader(mode)
for sequence_batch, optical_flow_batch, segmentation_targets, extrinsics, depth_tensor in tqdm(data_loader, desc=f"Visualizing {mode}"):
sequence_batch = sequence_batch.to(device)
optical_flow_batch = optical_flow_batch.to(device)
segmentation_targets = segmentation_targets.to(device)
extrinsics = extrinsics.to(device)
depth_tensor = depth_tensor.to(device)
predicted_segmentation_labels, predicted_depth, camera_poses_scaled = model_trainer.forward_pass(
sequence_batch, optical_flow_batch, extrinsics, mode
)
visualize_sequence_full(
sequence_batch, segmentation_targets, predicted_segmentation_labels, depth_tensor, predicted_depth, device
)
plot_camera_trajectories(extrinsics, camera_poses_scaled)
# GIFs Saving
input_images_sequence = []
gt_segmentation_sequence = []
predicted_segmentation_sequence = []
gt_depth_maps_sequence = []
predicted_depth_maps_sequence = []
batch_size, sequence_length, c, h, w = sequence_batch.size()
for i in range(sequence_length):
input_images_sequence.append(sequence_batch[0, i].permute(1, 2, 0).cpu().numpy())
gt_segmap = decode_segmap(segmentation_targets[0, i].cpu().numpy())
gt_segmentation_sequence.append(gt_segmap)
pred_segmap = decode_segmap(predicted_segmentation_labels[i].cpu().numpy())
predicted_segmentation_sequence.append(pred_segmap)
gt_depth_map = depth_tensor[0, i].squeeze().cpu().numpy()
if np.max(gt_depth_map) > 255 or np.min(gt_depth_map) < 0:
gt_depth_map = (gt_depth_map - np.min(gt_depth_map)) / (np.max(gt_depth_map) - np.min(gt_depth_map)) # Normalizing to 0-1
gt_depth_maps_sequence.append(gt_depth_map)
predicted_depth_map = predicted_depth[0, i].squeeze().cpu().detach().numpy()
if np.max(predicted_depth_map) > 255 or np.min(predicted_depth_map) < 0:
predicted_depth_map = (predicted_depth_map - np.min(predicted_depth_map)) / (np.max(predicted_depth_map) - np.min(predicted_depth_map)) # Normalizing to 0-1
predicted_depth_maps_sequence.append(predicted_depth_map)
if mode == "pretraining":
save_gif(input_images_sequence, os.path.join(GIF_path1, 'input_images.gif'))
save_gif(gt_segmentation_sequence, os.path.join(GIF_path1, 'gt_segmentation.gif'))
save_gif(predicted_segmentation_sequence, os.path.join(GIF_path1, 'predicted_segmentation.gif'))
save_gif(gt_depth_maps_sequence, os.path.join(GIF_path1, 'gt_depth_maps.gif'), duration=0.2)
save_gif(predicted_depth_maps_sequence, os.path.join(GIF_path1, 'predicted_depth_maps.gif'), duration=0.2)
else:
save_gif(input_images_sequence, os.path.join(GIF_path2, 'input_images.gif'))
save_gif(gt_segmentation_sequence, os.path.join(GIF_path2, 'gt_segmentation.gif'))
save_gif(predicted_segmentation_sequence, os.path.join(GIF_path2, 'predicted_segmentation.gif'))
save_gif(gt_depth_maps_sequence, os.path.join(GIF_path2, 'gt_depth_maps.gif'), duration=0.2)
save_gif(predicted_depth_maps_sequence, os.path.join(GIF_path2, 'predicted_depth_maps.gif'), duration=0.2)
break
# Visualize predictions for the pretrained model
print("Visualizing predictions from the pretrained model...")
visualize_predictions(trainer, pretrained_weights_path, trainer.org_val_loader, 'pretraining')
# Visualize predictions for the fine-tuned model
print("Visualizing predictions from the fine-tuned model...")
visualize_predictions(trainer, fine_tuned_weights_path, trainer.val_loader, 'fine_tuning')
Visualizing predictions from the pretrained model...
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Visualizing pretraining: 0%| | 0/8982 [00:12<?, ?it/s]
Visualizing predictions from the fine-tuned model...
Visualizing fine_tuning: 0%| | 0/8982 [00:00<?, ?it/s]
Visualizing fine_tuning: 0%| | 0/8982 [00:09<?, ?it/s]
7.3.1.3 GIFs¶
In [33]:
GIF_path1 = "GIF/NoFilter/Pretrained"
GIF_path2 = "GIF/NoFilter/Finetuned"
print("PreTrained Results without Filters at BaseCARLA")
display(Show_gifs(GIF_path1))
print("Finetuned Results with filter and at CorruptedCARLA")
display(Show_gifs(GIF_path2))
PreTrained Results without Filters at BaseCARLA
 |  |  |  |  |
Finetuned Results with filter and at CorruptedCARLA
 |  |  |  |  |
7.3.2 Training, Validation, Test Statistics¶
In [35]:
import matplotlib.pyplot as plt
import json
with open('Models/NoFilter/pretraining_stats.json', 'r') as f:
pretraining_stats = json.load(f)
with open('Models/NoFilter/finetuning_stats_stats.json', 'r') as f:
finetuning_stats = json.load(f)
epochs_pretraining = range(1, len(pretraining_stats['seg_loss_list']) + 1)
epochs_finetuning = range(len(epochs_pretraining), len(epochs_pretraining) + len(finetuning_stats['fseg_loss_list']))
plt.figure(figsize=(14, 10))
plt.subplot(2, 3, 1)
plt.plot(epochs_pretraining, pretraining_stats['mAcc_list'], label='Pretraining mAcc')
plt.plot(epochs_pretraining, pretraining_stats['val_mIoU_list'], label='Pretraining mIoU', linestyle='--')
plt.plot(epochs_finetuning, finetuning_stats['fmAcc_list'], label='Finetuning mAcc', linestyle='-.')
plt.plot(epochs_finetuning, finetuning_stats['fval_mIoU_list'], label='Finetuning mIoU', linestyle=':')
plt.xlabel('Epoch')
plt.ylabel('Metric Value')
plt.title('Mean Accuracy and IoU')
plt.legend()
def plot_loss_subplot(subplot_index, title, pretraining_loss, pretraining_val_loss, finetuning_loss, finetuning_val_loss):
plt.subplot(2, 3, subplot_index)
plt.plot(epochs_pretraining, pretraining_stats[pretraining_loss], label='Pretraining Training')
plt.plot(epochs_pretraining, pretraining_stats[pretraining_val_loss], label='Pretraining Validation', linestyle='--')
plt.plot(epochs_finetuning, finetuning_stats[finetuning_loss], label='Finetuning Training', linestyle='-.')
plt.plot(epochs_finetuning, finetuning_stats[finetuning_val_loss], label='Finetuning Validation', linestyle=':')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title(title)
plt.legend()
# Segmentation Losses
plot_loss_subplot(2, 'Segmentation Losses', 'seg_loss_list', 'seg_val_loss_list', 'fseg_loss_list', 'fseg_val_loss_list')
# Depth Losses
plot_loss_subplot(3, 'Depth Losses', 'depth_loss_list', 'depth_val_loss_list', 'fdepth_loss_list', 'fdepth_val_loss_list')
# Camera Pose Losses
plot_loss_subplot(4, 'Camera Pose Losses', 'cam_loss_list', 'cam_val_loss_list', 'fcam_loss_list', 'fcam_val_loss_list')
# Combined Model Losses
plot_loss_subplot(5, 'Overall Model Losses', 'total_loss_list', 'val_total_loss_list', 'ftotal_loss_list', 'fval_total_loss_list')
plt.tight_layout()
plt.show()
8 References¶
chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://courses.cs.washington.edu/courses/cse576/17sp/notes/Sachin_Talk.pdf
https://charisoudis.com/blog/a-simple-conv2d-dimensions-calculator-logger
chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.ais.uni-bonn.de/papers/BMVC_2018_Wagner.pdf